Dynamics and Decomposition:

Are They Compatible?

(from the Proceedings of the Australian Cognitive Science Society,
1997)

William Bechtel

Philosophy-Neuroscience-Psychology Program

Washington University in St. Louis

bechtel@twinearth.wustl.edu

Abstract

Much of cognitive neuroscience as well as traditional cognitive science
is engaged in a quest for mechanisms through a project of decomposition
and localization of cognitive functions. Some advocates of the emerging
dynamical systems approach to cognition construe it as in opposition to
the attempt to decompose and localize functions. I argue that this case
is not established and rather explore how dynamical systems tools can be
used to analyze and model cognitive functions without abandoning the use
of decomposition and localization to understand mechanisms of cognition.

Increasingly cognitive scientists are finding it very useful to employ
a dynamical perspective in modeling cognitive systems (Port and van Gelder,
1995). A dynamical perspective is attractive for a number of reasons. To
identify just three, it provides a way of modeling the highly interactive
nature of the mind/brain, it takes seriously the fact that cognitive processes
unfold in time, and it makes it easy to integrate the internal processes
of cognition with features of the environment, which are themselves changing
in the course of cognitive activity. I do not question the value of the
dynamical approach itself, but I do question one of the ways advocates
of dynamical systems approaches seek to contrast their approach with more
traditional approaches in cognitive science that are proving increasingly
important in cognitive neuroscience. These advocates contrast dynamical
approaches with mechanistic approaches to cognition--the attempt to identify
components or modules within cognitive systems and to construe cognitive
processes as involving the interaction of these modules. This challenge
construes a dynamics approach as advocating a radical holism. I will argue
against such holistic construals of the role of dynamical analysis within
cognitive science and contend that instead the dynamical approach ought
to be conceived of as compatible with a weak modularity.

To make the case that a dynamical conception of cognition is compatible
with mechanistic models, I begin with a brief account of mechanistic explanation.
I then turn to two sets of dynamicist arguments against mechanistic models,
due respectively to Timothy van Gelder and Guy van Orden, and propose answers
to both challenges. Lastly, I briefly present a case in which dynamical
tools are likely to be extremely important in the process of developing
a mechanistic explanation.

1. Mechanistic Explanation Through Decomposition and Localization

One of the claims that advocates of dynamical models of cognition
often make is that dynamical models have proven extremely powerful in providing
a framework for explaining a host of physical phenomena (van Gelder, 1995).
A similar claim, though, can be made about mechanistic models in biology.
What makes a model mechanistic, as I am using the term, is that it identifies
components in a system and attributes to these components responsibility
for performing specific tasks for the system. Mechanistic models have proven
powerful in providing explanations of a wide variety of phenomena such
as basic metabolism and genetics (both Mendelian and molecular). In Discovering
Complexity Robert Richardson and I focus on the process by which scientists
develop such models through a process we call decomposition and localization.
Decomposition involves identifying component tasks that need to be performed
in the system in order to accomplish the overall activities of the system,
while localization involves identifying (or providing evidence for) actual
components in the system that carry out these tasks. We trace out a common
path through which scientists develop such explanations. Often scientists
begin very simply, attributing an overall activity of the system to a single
component. We label this endeavor simple or direct localization.
In cognition, Broca's (1861) attribution of articulate speech to an area
in frontal cortex represents a proposed simple localization. A major virtue
of starting with such a simple account is that new evidence is often able
to make clear its shortcomings and provide evidence of how to revise it.
Such revision frequently involves identifying multiple components, each
performing only a component of the overall activity. This gives rise to
what we call complex localization. Many researchers took Broca to
have localized language function more generally, but Wernicke's (1874)
research, developed within the perspective of an associationist psychology,
identified a variety of language related tasks and localized them in different
areas. Complex localizations assume a linear feedforward processing system,
but frequently researchers begin to discover feedback processing in the
system as well. Such feedback, as the cyberneticists made clear (Wiener,
1948), provides a means whereby the overall system can self-regulate its
activity in the face of an often changing environment. Richardson and I
speak of such models as integrated systems.

The fact that mechanistic research often leads to models of integrated
systems will become important below when we consider the dynamicist challenge
to mechanist explanations that assume decomposition or modularity. For
now, though, it is sufficient to note one important aspect of such mechanistic
accounts: In identifying components, the components themselves are only
assumed to have first-order independence so that we can say what activities
each carries out. They are recognized to be interconnected, and these interactions
relate the components so that one component can modulate the activity of
others. As a result, these components are not Fodorian (Fodor, 1983) modules!

2. van Gelder's Argument for Rejecting Homuncularity

The components isolated in a mechanistic model have been characterized
by Dennett (1977) as homunculi--little agents each responsible for one
operation in the overall activity of the system. In his presentation of
the potential of the dynamics approach to radically alter cognitive science,
van Gelder associates homuncularity with representation, computation, sequential
and cyclic operation: "a device with any one of them will standardly posses
others" (1995, p.351). He then proceeds to contrast an approach to cognition
that assumes these features with a dynamical approach. Here I will focus
only on van Gelder's denial of homuncularity to dynamical models (in Bechtel,
in press, I examine and criticize his denial of representations to dynamical
models).

To make his account concrete, van Gelder (1995) compares two models
for regulating a steam engine: the Watt centrifugal governor and a computational
governor. The Watt governor (Figure 1) employs a spindle mounted on the
flywheel and angle arms attached to the spindle. As the flywheel moves
faster, the arms move outwards. A linkage system connects these arms to
a steam valve in such a way as to begin to close the valve when the the
arms move out (as a result of the flywheel turning faster) and to open
the steam valve as the arms drop. Thus, when the flywheel starts to turn
too fast, the steam flow is reduced so as to slow it down, and when it
starts to turn too slowly, the steam flow is increased so as to speed it
up. The Watt governor is contrasted with a hypothetical computational governor
which might be implemented in a traditional computer program that carried
out the following operations:

1. Measure the speed of the flywheel

2. Compare the actual speed against the desired speed

3. If there is no discrepancy, return to step 1. Otherwise,

a. measure the current steam pressure;

b. calculate the desired alteration in steam pressure;

c. calculate the necessary throttle valve adjustment.

4. Make the throttle valve adjustment.

Return to step 1. (van Gelder, 1995, p. 348)

The computational governor is construed as a homuncular system since there
are distinct operations which could be carried out by distinct homunculi,
while by implication the Watt governor is not homuncular.

Figure 1. Watt's Centrifugal Governor
for the steam engine on the left and a schamatic representation of the
functioning of the governor on the right.

However, a careful examination of the Watt governor reveals that it
is homuncular in just the sense required for a mechanistic explanation.
The Watt governor has a variety of different parts: the flywheel, the spindle
and angle arms, and a linkage mechanism connected to a valve controlling
the steam. As Figure 1 makes clear, these components in fact do different
jobs: the valve controls the amount of steam driving the flywheel, the
flywheel turns with a specific speed, and the angle arms move out with
centrifugal force. This becomes very clear if we attempt to explain how
the Watt governor works and why each of the parts are needed--we explain
their function in terms of what task they perform for the overall system.
For example, we explain the need for the angle arms in terms of the need
to transform the information about the speed of the flywheel into a format
that can be used to regulate the opening of the steam valve by a linkage
mechanism.

It is true, and an important point, that the tasks performed by the
parts of the Watt governor do not map onto the operations of the homunculi
in the computational governor. Moreover, the vocabulary for describing
the parts is that appropriate for the task they do: angle arms move in
an out according to centrifugal force, etc. We do not directly describe
the activity of the parts in terms of the overall activity of the governor
in regulating speed or in terms of tasks that would be identified in an
a priori task analysis. We shift vocabularies from one describing the overall
steam engine's behavior to one describing what the parts do. There is then
an extra step to connect these processes to the needs of the overall system--we
have to note that the angle of the arms carries information about the speed
of the flywheel in a format appropriate to the mechanism that will open
and shut the valve on the steam engine.

This important point applies as well to the brain--in understanding
the brain we need to consider what the component systems in the brain themselves
do, not impose an a priori task analysis drawn from the cognitive activities
we do with our brain. Terrence Deacon (1997) makes this clear when he refers
to seeking a neural logic, not linguistic logic when we try to explain
how language operations are performed in the brain:

Once we abandon the idealization that language is plugged into the
brain in modules, and recognize it as merely a new use of existing structures,
there is no reason to expect that language functions should map in any
direct way on the structural-functional divisions of cortex. It is far
more reasonable to expect language processes to be broken up into subfunctions
that have more to do with neural logic than with linguistic logic. . .
. Language is fitted to an ape brain plan, and our ape brains, though modified
in response as well, are by far the more inflexible element in this relationship
(1997, p. 287).

Thus, van Gelder's distinction between the Watt governor and the computational
governor is important. There are different ways to decompose the task performed
by the system, and ones put forward as a result of an a priori task analysis
may not be the best ones either for engineering or for understanding natural
systems. But, and this is also crucial, both the Watt governor and the
computational governor are humuncular and identifying the tasks performed
by the components is critical to understanding either system.

Another line of attack on mechanistic decomposition on behalf of a dynamical
systems approach is developed by van Orden et al. (in preparation). The
main target of their critique is the use of double-dissociation studies
to decompose cognitive processes into different processing streams (e.g.,
a lexical and a sub-lexical route in reading), but they also extend the
critique to the use of substraction methodologies in neuroimaging so to
identify brain areas involved in cognitive performance (van Orden and Papp,
1997). In developing the critique, van Orden et al. draw a contrast between
approaches that assume single causes for individual cognitive activities
(double-dissociation studies and neuroimaging), and those that assume reciprocal
causality (dynamical models). There are some important methodological
points raised in this critique of double dissociation. One is that double
dissociation studies assume separate causes for separate effects (if two
effects are separate, then their causes are separate causes). The development
of connectionist models that generate double-dissociations from single
connectionist networks (Plaut et al., 1996) effectively establishes that
a double dissociation does not alone demonstrate the existence of totally
separate processing pathways. A second is that the search for pure cases
of double-dissociations (one that shows a deficit in one behavior and no
deficits in the other, and one that shows the reverse pattern) can be non-terminating
and thus risks giving rise to a degenerating research program.

What is noteworthy, and problematic about van Orden et al.'s approach,
though, is that they set up an overly strong version of the decompositional
or modular perspective. They claim that

before modularity can hold between two cognitive components it is necessary
that the following conditions hold:

CONDITION 2b: They relate the same inputs to nonoverlapping sets of
outputs. . . .

OR

CONDITION 2c: They relate nonoverlapping sets of inputs to the same
outputs. . ." (p. 40)

Thus, they insist upon fully isolated processing streams. Equally, they
advance an extremely holistic version of the dynamical or reciprocal causality
model:

A key entailment of reciprocal causality is that every behavior involves
interaction among every component of the system. . . . Componential theorists
sometimes misunderstand the fact that traditional components may interact
to mean that traditional assumptions are in some way reconcilable with
fully interdependent systems. This misunderstanding probably stems, in
part, from the familiar statistical distinction between main effects and
interaction effects (strictly additive main effects may originate in separate
single causes, Sternberg, 1969). However, accepting interdependent components
as a working hypothesis entails that every effect is part of a larger
pattern of "interaction" (there are no "main" effects). (p. 37)

For van Orden et al, one thing that makes it seem likely that the cognitive
system is holistic is the kind of highly interconnected wiring pattern
found in the brain, especially the prominent role played by recurrent connections.

We can now identify the problems in van Orden et al.'s critique. The
problem stems from the two extremes put forward, and the failure to consider
more intermediate cases. Thus, on the one hand their account of decomposition
or modularity only considers what I earlier labeled simple or complex
localizations, not integrated systems. Accordingly, it ignores
more sophisticated mechanistic models which allow for extensive interaction
between the separate processing components. In integrated systems, components
still carry out specific tasks, but they do so in a highly interactive
fashion. A powerful example of a model of an integrated system is Felleman
and van Essen's (1991) model of visual processing. These researchers identify
32 different brain regions in the visual processing system of the macaque
and roughly 300 sets of connections between these areas. Thus, these regions
are highly interconnected, with approximately one-third of the possible
interconnections actually realized (the vast majority involving bidirectional
connections). Despite the fact that this system is highly interconnected,
the different brain regions are thought to carry out different tasks (as
determined by single cell recording and more recently neuroimaging). Thus,
different areas process color , others shape, and movement information.
The interactions in this system make it a natural subject for a dynamical
analysis, but it is also a case of a mechanistic system. Thus, even without
single causes or isolated pathways, one can have a form of weak modularity
in which components make different contributions even while sharing information.
(One point that should be noted about recurrent connections in the brain
is that they arise from different layers of cortex and project to different
layers of cortex than do feedforward connections. This provides just one
among several reasons for thinking they perform different tasks. This do
not merely function to achieve holistic integration.)

A similar objection can be raised to the perspective on dynamics that
van Orden et al. advocate. The strong holist claims they advance stems
from assuming a system that is totally interconnected. Increasingly, however,
neural network researchers are moving away from totally interconnected
networks to more modular networks in which specific processing components
are primarily responsible for processing different kinds of information.
In one clear example Jacobs, Jordan, and Barto (1991) developed a network
system comprising three separate feedforward networks and a gating network
that determines which of the competing networks responds to a particular
input. They show that in a simple version of a task requiring identification
of either the location or the identity of an input letter, the system assigned
one network without hidden units to the location task and another network
with hidden units to the identification task. This distribution of tasks
arose during learning; it was not engineered. Not only does a modular design
seem to give greater efficiency than a nonmodular system, but it also avoided
serious known liabilities of network systems such as catastrophic interference.
Thus, even within connectionist frameworks that generally emphasize a high
degree of integration, there is evidence of task decomposition. (This can
even be found in connectionist networks that challenge the claims of multiple
routes based on double-dissociations: these networks can mimic the effects
of double-dissociation precisely because different parts of the networks
are carrying out different processing tasks and so create different deficits
when lesioned.)

4. Using Dynamical Tools in a Decompositional Research Program

My strategy has been to question the dynamicist claim that a dynamical
approach is incompatible with a mechanistic conception of a system which
emphasizes decomposition and localization. In this last section, I briefly
point to the kind of problems that arise in mechanistic systems that make
it natural to invoke dynamical tools within a mechanistic framework. To
do this I will return to the case of language processing in the brain.
Earlier I identified Broca's approach to language processing as constituting
a simple localization and Wernicke's project as pointing to a complex localization.
Recently Deacon (1997) has provided a perspective that suggests a much
more integrated model. Above I quoted his suggestion that we view language
as grafted onto a primate brain; in following this path Deacon appeals
to evidence that electrical stimulation used to inject noise into different
parts of the frontal, parietal, and temporal cortex interrupts various
aspects of language processing. From this he argues that a wide range of
brain areas are involved. Two points, though, are especially noteworthy:
stimulation in different areas produces different interruptions, and there
is a pattern of concentric regions that produce the same kinds of disruptions.
Thus, stimulation to areas closest to motor cortex interfere with motor
activities of speech, but areas both anterior and posterior to this begin
to affect syntactic and semantic processes. Deacon advances an intriguing
framework to account for this and other evidence. He hypothesizes that
the areas that affect the same activity are joined in recurrent connections,
possibly creating different attractor systems. As the network settles,
it passes information on to areas closer to the actual motor control involved
in speech. The areas closer to each other can settle into attractors much
more quickly than those that are more separated, and this difference in
timing may have functional significance.

Slow neural signal transmission can also become a limiting factor in
the brain, so that very rapid processes are best handled within a very
localized region, whereas the accumulation of information over time might
better be served by a more distributed and redundant organization that
resists degradation. So it makes sense that for each modality, it might
be advantageous to segregate its fast from its slow processes (1997, p.
291).

Modeling such systems with highly recurrent internal connections and
slow feedforward to other processing components is the precise sort of
task for which dynamical tools are required. And they are required in the
course of modeling a system with components carrying out different activities--a
mechanistic system.

5. Conclusions

Dynamical approaches are not incompatible with mechanistic explanations
developed through decomposition and localization, but in fact have an important
role to play within mechanistic research. The appearance of incompatibility
results from an excessive holism in the characterization of dynamical models
and a failure to recognize the sort of integrated mechanisms that result
from decomposition and localization.